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apply Black 25.11.0 style in fbcode/deeplearning/projects (21/92)

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Summary:
Formats the covered files with pyfmt.

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Reviewed By: itamaro

Differential Revision: D90476315

fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f
This commit is contained in:
Bowie Chen
2026-01-11 23:16:49 -08:00
committed by meta-codesync[bot]
parent 7b89b8fc3f
commit 11dec2936d
69 changed files with 445 additions and 522 deletions
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37
sam3/model/sam3_video_base.py
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@@ -16,7 +16,6 @@ import numpy.typing as npt
import torch
import torch.distributed as dist
import torch.nn.functional as F
from sam3 import perflib
from sam3.logger import get_logger
from sam3.model.box_ops import fast_diag_box_iou
@@ -620,9 +619,9 @@ class Sam3VideoBase(nn.Module):
num_obj_dropped_due_to_limit,
trk_id_to_max_iou_high_conf_det,
]
assert (
len(update_plan) == NUM_BROADCAST_ITEMS
), f"Manually update NUM_BROADCAST_ITEMS to be: {len(update_plan)}"
assert len(update_plan) == NUM_BROADCAST_ITEMS, (
f"Manually update NUM_BROADCAST_ITEMS to be: {len(update_plan)}"
)
self.broadcast_python_obj_cpu(update_plan, src=0)
elif self.rank > 0 and self.world_size > 1:
update_plan = [
@@ -842,9 +841,9 @@ class Sam3VideoBase(nn.Module):
binary_tracker_low_res_masks_global = tracker_low_res_masks_global > 0
batch_size = tracker_low_res_masks_global.size(0)
if batch_size > 0:
assert (
len(obj_ids_global) == batch_size
), f"Mismatch in number of objects: {len(obj_ids_global)} vs {batch_size}"
assert len(obj_ids_global) == batch_size, (
f"Mismatch in number of objects: {len(obj_ids_global)} vs {batch_size}"
)
NEVER_OCCLUDED = -1
ALWAYS_OCCLUDED = 100000 # This value should be larger than any possible frame index, indicates that the object was removed by hotstart logic
last_occluded_prev = torch.cat(
@@ -1023,9 +1022,9 @@ class Sam3VideoBase(nn.Module):
reverse: bool = False,
):
# Suppress overlapping masks for objects that were most recently occluded
assert (
binary_low_res_masks.dtype == torch.bool
), f"Expected boolean tensor, got {binary_low_res_masks.dtype}"
assert binary_low_res_masks.dtype == torch.bool, (
f"Expected boolean tensor, got {binary_low_res_masks.dtype}"
)
to_suppress = torch.zeros(
binary_low_res_masks.size(0),
device=binary_low_res_masks.device,
@@ -1130,9 +1129,9 @@ class Sam3VideoBase(nn.Module):
num_frames_propagated += 1
# only 1 frames should be propagated
assert (
num_frames_propagated == 1 and out_frame_idx == frame_idx
), f"num_frames_propagated: {num_frames_propagated}, out_frame_idx: {out_frame_idx}, frame_idx: {frame_idx}"
assert num_frames_propagated == 1 and out_frame_idx == frame_idx, (
f"num_frames_propagated: {num_frames_propagated}, out_frame_idx: {out_frame_idx}, frame_idx: {frame_idx}"
)
assert isinstance(out_obj_ids, list)
obj_ids_local.extend(out_obj_ids)
low_res_masks_list.append(out_low_res_masks.squeeze(1))
@@ -1189,9 +1188,9 @@ class Sam3VideoBase(nn.Module):
assert det_masks.is_floating_point(), "float tensor expected (do not binarize)"
assert trk_masks.is_floating_point(), "float tensor expected (do not binarize)"
assert (
trk_masks.size(0) == len(trk_obj_ids)
), f"trk_masks and trk_obj_ids should have the same length, {trk_masks.size(0)} vs {len(trk_obj_ids)}"
assert trk_masks.size(0) == len(trk_obj_ids), (
f"trk_masks and trk_obj_ids should have the same length, {trk_masks.size(0)} vs {len(trk_obj_ids)}"
)
if trk_masks.size(0) == 0:
# all detections are new
new_det_fa_inds = np.arange(det_masks.size(0))
@@ -1655,9 +1654,9 @@ class Sam3VideoBase(nn.Module):
# a) first, expand "confirmation_data" to include new masklets added in this frame
status_prev = confirmation_data["status"]
consecutive_det_num_prev = confirmation_data["consecutive_det_num"]
assert (
status_prev.shape == obj_ids_all_gpu_prev.shape
), f"Got {status_prev.shape} vs {obj_ids_all_gpu_prev.shape}"
assert status_prev.shape == obj_ids_all_gpu_prev.shape, (
f"Got {status_prev.shape} vs {obj_ids_all_gpu_prev.shape}"
)
obj_id_to_updated_idx = {
obj_id: idx for idx, obj_id in enumerate(obj_ids_all_gpu_updated)